Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations12791
Missing cells0
Missing cells (%)0.0%
Duplicate rows481
Duplicate rows (%)3.8%
Total size in memory2.4 MiB
Average record size in memory200.0 B

Variable types

Numeric8
Categorical16

Alerts

Dataset has 481 (3.8%) duplicate rowsDuplicates
area_type_Plot Area is highly overall correlated with area_type_Super built-up Area and 3 other fieldsHigh correlation
area_type_Super built-up Area is highly overall correlated with area_type_Plot AreaHigh correlation
balcony is highly overall correlated with balcony_per_bhkHigh correlation
balcony_per_bhk is highly overall correlated with balcony and 1 other fieldsHigh correlation
bath is highly overall correlated with bhk and 1 other fieldsHigh correlation
bhk is highly overall correlated with area_type_Plot Area and 2 other fieldsHigh correlation
price is highly overall correlated with price_per_balcony and 3 other fieldsHigh correlation
price_per_balcony is highly overall correlated with balcony_per_bhk and 3 other fieldsHigh correlation
price_per_bhk is highly overall correlated with price and 4 other fieldsHigh correlation
price_per_sqft is highly overall correlated with area_type_Plot Area and 3 other fieldsHigh correlation
sqft_per_bhk is highly overall correlated with area_type_Plot Area and 1 other fieldsHigh correlation
total_sqft is highly overall correlated with bath and 3 other fieldsHigh correlation
area_type_Carpet Area is highly imbalanced (94.1%)Imbalance
location_Hebbal is highly imbalanced (90.3%)Imbalance
location_Kanakpura Road is highly imbalanced (86.2%)Imbalance
location_Marathahalli is highly imbalanced (89.9%)Imbalance
location_Raja Rajeshwari Nagar is highly imbalanced (90.3%)Imbalance
location_Sarjapur Road is highly imbalanced (80.7%)Imbalance
location_Thanisandra is highly imbalanced (87.0%)Imbalance
location_Uttarahalli is highly imbalanced (90.4%)Imbalance
location_Whitefield is highly imbalanced (75.3%)Imbalance
location_Yelahanka is highly imbalanced (87.9%)Imbalance
balcony_per_bhk has 1010 (7.9%) zerosZeros

Reproduction

Analysis started2025-09-29 19:04:52.749726
Analysis finished2025-09-29 19:04:55.543990
Duration2.79 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

total_sqft
Real number (ℝ)

High correlation 

Distinct1576
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.433002
Minimum10.093058
Maximum20.704396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:55.572429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.093058
5-th percentile14.255924
Q116.429999
median17.139836
Q318.490831
95-th percentile20.704396
Maximum20.704396
Range10.611338
Interquartile range (IQR)2.060832

Descriptive statistics

Standard deviation1.7953381
Coefficient of variation (CV)0.10298502
Kurtosis0.14356239
Mean17.433002
Median Absolute Deviation (MAD)0.95762138
Skewness0.0060617545
Sum222985.52
Variance3.2232389
MonotonicityNot monotonic
2025-09-30T00:34:55.608814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.704395681157
 
9.0%
16.83050085808
 
6.3%
16.4299992210
 
1.6%
17.8927831203
 
1.6%
20.30552975196
 
1.5%
13.84003241178
 
1.4%
15.99987746168
 
1.3%
17.38480874128
 
1.0%
17.02101686111
 
0.9%
17.20565368111
 
0.9%
Other values (1566)9521
74.4%
ValueCountFrequency (%)
10.093058128
0.1%
10.651428621
 
< 0.1%
10.76027611
 
< 0.1%
10.982221951
 
< 0.1%
11.079080491
 
< 0.1%
11.22043631
 
< 0.1%
11.266556991
 
< 0.1%
11.289436921
 
< 0.1%
11.435331411
 
< 0.1%
11.703128221
 
< 0.1%
ValueCountFrequency (%)
20.704395681157
9.0%
20.703333511
 
< 0.1%
20.682056241
 
< 0.1%
20.677793021
 
< 0.1%
20.675660432
 
< 0.1%
20.656437824
 
< 0.1%
20.65429873
 
< 0.1%
20.650018512
 
< 0.1%
20.647877442
 
< 0.1%
20.635017173
 
< 0.1%

bath
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0.7441364167836307
6619 
1.2303648987791047
3180 
1.6003717123308412
1198 
1.7581883209318796
1032 
9.999996011388275e-07
762 

Length

Max length21
Median length18
Mean length18.178719
Min length18

Characters and Unicode

Total characters232524
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.7441364167836307
2nd row1.7581883209318796
3rd row0.7441364167836307
4th row1.2303648987791047
5th row0.7441364167836307

Common Values

ValueCountFrequency (%)
0.74413641678363076619
51.7%
1.23036489877910473180
24.9%
1.60037171233084121198
 
9.4%
1.75818832093187961032
 
8.1%
9.999996011388275e-07762
 
6.0%

Length

2025-09-30T00:34:55.641738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:55.669072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.74413641678363076619
51.7%
1.23036489877910473180
24.9%
1.60037171233084121198
 
9.4%
1.75818832093187961032
 
8.1%
9.999996011388275e-07762
 
6.0%

Most occurring characters

ValueCountFrequency (%)
735381
15.2%
332637
14.0%
129010
12.5%
427415
11.8%
626029
11.2%
025748
11.1%
819829
8.5%
912996
 
5.6%
.12791
 
5.5%
27370
 
3.2%
Other values (3)3318
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)232524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
735381
15.2%
332637
14.0%
129010
12.5%
427415
11.8%
626029
11.2%
025748
11.1%
819829
8.5%
912996
 
5.6%
.12791
 
5.5%
27370
 
3.2%
Other values (3)3318
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)232524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
735381
15.2%
332637
14.0%
129010
12.5%
427415
11.8%
626029
11.2%
025748
11.1%
819829
8.5%
912996
 
5.6%
.12791
 
5.5%
27370
 
3.2%
Other values (3)3318
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)232524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
735381
15.2%
332637
14.0%
129010
12.5%
427415
11.8%
626029
11.2%
025748
11.1%
819829
8.5%
912996
 
5.6%
.12791
 
5.5%
27370
 
3.2%
Other values (3)3318
 
1.4%

balcony
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
1.0
5710 
2.0
4846 
3.0
1630 
1.5823075660594124
605 

Length

Max length18
Median length3
Mean length3.7094832
Min length3

Characters and Unicode

Total characters47448
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05710
44.6%
2.04846
37.9%
3.01630
 
12.7%
1.5823075660594124605
 
4.7%

Length

2025-09-30T00:34:55.700635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:55.720475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.05710
44.6%
2.04846
37.9%
3.01630
 
12.7%
1.5823075660594124605
 
4.7%

Most occurring characters

ValueCountFrequency (%)
013396
28.2%
.12791
27.0%
16920
14.6%
26056
12.8%
32235
 
4.7%
51815
 
3.8%
61210
 
2.6%
41210
 
2.6%
8605
 
1.3%
7605
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)47448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013396
28.2%
.12791
27.0%
16920
14.6%
26056
12.8%
32235
 
4.7%
51815
 
3.8%
61210
 
2.6%
41210
 
2.6%
8605
 
1.3%
7605
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013396
28.2%
.12791
27.0%
16920
14.6%
26056
12.8%
32235
 
4.7%
51815
 
3.8%
61210
 
2.6%
41210
 
2.6%
8605
 
1.3%
7605
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013396
28.2%
.12791
27.0%
16920
14.6%
26056
12.8%
32235
 
4.7%
51815
 
3.8%
61210
 
2.6%
41210
 
2.6%
8605
 
1.3%
7605
 
1.3%

price
Real number (ℝ)

High correlation 

Distinct1994
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.31765
Minimum8
Maximum3600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:55.748825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile31.845
Q150
median73
Q3121
95-th percentile325
Maximum3600
Range3592
Interquartile range (IQR)71

Descriptive statistics

Standard deviation151.48031
Coefficient of variation (CV)1.3250825
Kurtosis104.91489
Mean114.31765
Median Absolute Deviation (MAD)28
Skewness7.9543544
Sum1462237
Variance22946.284
MonotonicityNot monotonic
2025-09-30T00:34:55.784635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75300
 
2.3%
65296
 
2.3%
55265
 
2.1%
60263
 
2.1%
45231
 
1.8%
50225
 
1.8%
70208
 
1.6%
90199
 
1.6%
85192
 
1.5%
110182
 
1.4%
Other values (1984)10430
81.5%
ValueCountFrequency (%)
81
 
< 0.1%
8.441
 
< 0.1%
91
 
< 0.1%
106
< 0.1%
10.252
 
< 0.1%
10.51
 
< 0.1%
112
 
< 0.1%
11.54
< 0.1%
11.661
 
< 0.1%
125
< 0.1%
ValueCountFrequency (%)
36001
< 0.1%
29121
< 0.1%
28001
< 0.1%
27361
< 0.1%
27001
< 0.1%
26002
< 0.1%
23401
< 0.1%
22501
< 0.1%
22002
< 0.1%
21001
< 0.1%

bhk
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0.8339994034381853
5261 
1.480245266106506
4655 
2.0284814252466012
1398 
2.2776160769759706
839 
9.999997590520362e-07
638 

Length

Max length21
Median length18
Mean length17.785709
Min length17

Characters and Unicode

Total characters227497
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.8339994034381853
2nd row2.0284814252466012
3rd row1.480245266106506
4th row1.480245266106506
5th row0.8339994034381853

Common Values

ValueCountFrequency (%)
0.83399940343818535261
41.1%
1.4802452661065064655
36.4%
2.02848142524660121398
 
10.9%
2.2776160769759706839
 
6.6%
9.999997590520362e-07638
 
5.0%

Length

2025-09-30T00:34:55.818345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:55.844257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.83399940343818535261
41.1%
1.4802452661065064655
36.4%
2.02848142524660121398
 
10.9%
2.2776160769759706839
 
6.6%
9.999997590520362e-07638
 
5.0%

Most occurring characters

ValueCountFrequency (%)
030875
13.6%
326943
11.8%
625410
11.2%
424026
10.6%
823234
10.2%
921927
9.6%
219254
8.5%
118206
8.0%
518084
7.9%
.12791
5.6%
Other values (3)6747
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)227497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
030875
13.6%
326943
11.8%
625410
11.2%
424026
10.6%
823234
10.2%
921927
9.6%
219254
8.5%
118206
8.0%
518084
7.9%
.12791
5.6%
Other values (3)6747
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)227497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
030875
13.6%
326943
11.8%
625410
11.2%
424026
10.6%
823234
10.2%
921927
9.6%
219254
8.5%
118206
8.0%
518084
7.9%
.12791
5.6%
Other values (3)6747
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)227497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
030875
13.6%
326943
11.8%
625410
11.2%
424026
10.6%
823234
10.2%
921927
9.6%
219254
8.5%
118206
8.0%
518084
7.9%
.12791
5.6%
Other values (3)6747
 
3.0%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct7038
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0784501
Minimum-3.9317289
Maximum-1.6699644
Zeros0
Zeros (%)0.0%
Negative12791
Negative (%)100.0%
Memory size199.9 KiB
2025-09-30T00:34:55.878183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.9317289
5-th percentile-2.3810368
Q1-2.2302644
median-2.1099144
Q3-1.9517808
95-th percentile-1.6699644
Maximum-1.6699644
Range2.2617645
Interquartile range (IQR)0.27848366

Descriptive statistics

Standard deviation0.21847231
Coefficient of variation (CV)-0.10511309
Kurtosis1.3898708
Mean-2.0784501
Median Absolute Deviation (MAD)0.13315691
Skewness0.082542028
Sum-26585.456
Variance0.047730152
MonotonicityNot monotonic
2025-09-30T00:34:55.914414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.6699643831208
 
9.4%
-2.155947535142
 
1.1%
-2.264096669123
 
1.0%
-1.78177165277
 
0.6%
-2.00795316871
 
0.6%
-2.32617402563
 
0.5%
-2.04202307457
 
0.4%
-2.39547452452
 
0.4%
-2.06330984352
 
0.4%
-2.10801171250
 
0.4%
Other values (7028)10896
85.2%
ValueCountFrequency (%)
-3.9317288971
< 0.1%
-3.6767933641
< 0.1%
-3.6386260451
< 0.1%
-3.6261685651
< 0.1%
-3.5799005431
< 0.1%
-3.5684094361
< 0.1%
-3.3569974251
< 0.1%
-3.259695931
< 0.1%
-3.2510090231
< 0.1%
-3.2110256651
< 0.1%
ValueCountFrequency (%)
-1.6699643831208
9.4%
-1.670283771
 
< 0.1%
-1.6707904841
 
< 0.1%
-1.6709304841
 
< 0.1%
-1.6711047141
 
< 0.1%
-1.6714142761
 
< 0.1%
-1.6714404451
 
< 0.1%
-1.6722949841
 
< 0.1%
-1.672394161
 
< 0.1%
-1.67288633922
 
0.2%

sqft_per_bhk
Real number (ℝ)

High correlation 

Distinct1650
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.02637
Minimum184.52022
Maximum609.39943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:55.949286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum184.52022
5-th percentile199.20284
Q1347.58035
median402.1575
Q3453.43138
95-th percentile609.39943
Maximum609.39943
Range424.87921
Interquartile range (IQR)105.85103

Descriptive statistics

Standard deviation100.79954
Coefficient of variation (CV)0.25010656
Kurtosis0.096526731
Mean403.02637
Median Absolute Deviation (MAD)52.976461
Skewness0.0025866758
Sum5155110.2
Variance10160.547
MonotonicityNot monotonic
2025-09-30T00:34:55.981862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
609.3994312787
 
6.2%
184.520218602
 
4.7%
434.6758369513
 
4.0%
366.0208858329
 
2.6%
400.4415128251
 
2.0%
296.536683244
 
1.9%
225.9975787182
 
1.4%
331.3940925181
 
1.4%
383.2556371143
 
1.1%
468.74030898
 
0.8%
Other values (1640)9461
74.0%
ValueCountFrequency (%)
184.520218602
4.7%
185.00078471
 
< 0.1%
188.78251631
 
< 0.1%
189.14243083
 
< 0.1%
190.221915521
 
0.2%
191.18113331
 
< 0.1%
192.61939191
 
< 0.1%
193.09866071
 
< 0.1%
193.45806311
 
< 0.1%
194.17674181
 
< 0.1%
ValueCountFrequency (%)
609.3994312787
6.2%
608.5637761
 
< 0.1%
608.45234981
 
< 0.1%
607.78022941
 
< 0.1%
607.72804811
 
< 0.1%
606.89224753
 
< 0.1%
606.55790682
 
< 0.1%
606.30714371
 
< 0.1%
605.77773333
 
< 0.1%
605.55481492
 
< 0.1%

bath_per_bhk
Real number (ℝ)

Distinct48
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96823876
Minimum0.25
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:56.101396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.66666667
Q11
median1
Q31
95-th percentile1.25
Maximum2
Range1.75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18056613
Coefficient of variation (CV)0.18648927
Kurtosis5.8034103
Mean0.96823876
Median Absolute Deviation (MAD)0
Skewness0.55047651
Sum12384.742
Variance0.032604129
MonotonicityNot monotonic
2025-09-30T00:34:56.136242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
19678
75.7%
0.66666666671492
 
11.7%
1.333333333297
 
2.3%
0.5271
 
2.1%
1.25266
 
2.1%
0.75211
 
1.6%
1.5149
 
1.2%
0.892
 
0.7%
255
 
0.4%
1.235
 
0.3%
Other values (38)245
 
1.9%
ValueCountFrequency (%)
0.251
 
< 0.1%
0.28571428571
 
< 0.1%
0.33333333332
 
< 0.1%
0.3751
 
< 0.1%
0.47
 
0.1%
0.42857142862
 
< 0.1%
0.5271
2.1%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857148
 
0.1%
ValueCountFrequency (%)
255
 
0.4%
1.751
 
< 0.1%
1.66666666735
 
0.3%
1.61
 
< 0.1%
1.5555555561
 
< 0.1%
1.5149
1.2%
1.4444444441
 
< 0.1%
1.415
 
0.1%
1.333333333297
2.3%
1.31
 
< 0.1%

balcony_per_bhk
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61754245
Minimum0
Maximum3
Zeros1010
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:56.167812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.39557689
median0.5
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0.60442311

Descriptive statistics

Standard deviation0.3392433
Coefficient of variation (CV)0.5493441
Kurtosis0.76708771
Mean0.61754245
Median Absolute Deviation (MAD)0.16666667
Skewness0.42262757
Sum7898.9855
Variance0.11508602
MonotonicityNot monotonic
2025-09-30T00:34:56.198199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
13179
24.9%
0.53099
24.2%
0.66666666672198
17.2%
0.33333333331296
10.1%
01010
 
7.9%
0.25345
 
2.7%
1.5325
 
2.5%
0.75278
 
2.2%
0.5274358554199
 
1.6%
0.3955768915177
 
1.4%
Other values (27)685
 
5.4%
ValueCountFrequency (%)
01010
7.9%
0.083279345581
 
< 0.1%
0.087905975891
 
< 0.1%
0.098894222881
 
< 0.1%
0.11111111119
 
0.1%
0.12510
 
0.1%
0.142857142915
 
0.1%
0.14384614241
 
< 0.1%
0.158230756611
 
0.1%
0.166666666740
 
0.3%
ValueCountFrequency (%)
32
 
< 0.1%
235
 
0.3%
1.5823075668
 
0.1%
1.5325
 
2.5%
13179
24.9%
0.79115378364
 
0.5%
0.75278
 
2.2%
0.66666666672198
17.2%
0.660
 
0.5%
0.5274358554199
 
1.6%

price_per_bhk
Real number (ℝ)

High correlation 

Distinct1991
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.023447
Minimum1.1536309
Maximum3.680636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:56.230840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1536309
5-th percentile2.4435874
Q12.7632452
median3.0017503
Q33.2740065
95-th percentile3.680636
Maximum3.680636
Range2.5270051
Interquartile range (IQR)0.51076132

Descriptive statistics

Standard deviation0.36954021
Coefficient of variation (CV)0.1222248
Kurtosis-0.3187763
Mean3.023447
Median Absolute Deviation (MAD)0.24572562
Skewness0.0038539946
Sum38672.911
Variance0.13655997
MonotonicityNot monotonic
2025-09-30T00:34:56.265415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6806359971072
 
8.4%
2.875472615361
 
2.8%
3.019388067342
 
2.7%
2.696763178257
 
2.0%
3.242690282232
 
1.8%
2.950939556221
 
1.7%
2.791447398211
 
1.6%
3.139612415198
 
1.5%
3.081979832175
 
1.4%
3.412762854171
 
1.3%
Other values (1981)9551
74.7%
ValueCountFrequency (%)
1.1536308991
< 0.1%
1.3198358241
< 0.1%
1.3682338251
< 0.1%
1.5637423091
< 0.1%
1.6817869541
< 0.1%
1.6999146561
< 0.1%
1.7176502282
< 0.1%
1.7350097441
< 0.1%
1.7849787741
< 0.1%
1.8682649181
< 0.1%
ValueCountFrequency (%)
3.6806359971072
8.4%
3.6771935422
 
< 0.1%
3.6746019731
 
< 0.1%
3.6704350571
 
< 0.1%
3.6693893761
 
< 0.1%
3.66413701641
 
0.3%
3.6614957411
 
< 0.1%
3.6570709951
 
< 0.1%
3.6535106354
 
< 0.1%
3.6508282494
 
< 0.1%

price_per_balcony
Real number (ℝ)

High correlation 

Distinct2156
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7046978
Minimum1.5709469
Maximum4.6655245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.9 KiB
2025-09-30T00:34:56.297847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5709469
5-th percentile2.7929004
Q13.2763266
median3.6897499
Q34.1044252
95-th percentile4.6655245
Maximum4.6655245
Range3.0945776
Interquartile range (IQR)0.82809862

Descriptive statistics

Standard deviation0.57496975
Coefficient of variation (CV)0.15520018
Kurtosis-0.73841395
Mean3.7046978
Median Absolute Deviation (MAD)0.41467532
Skewness0.0048494485
Sum47386.789
Variance0.33059021
MonotonicityNot monotonic
2025-09-30T00:34:56.332524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6655244791159
 
9.1%
4.047832168208
 
1.6%
3.689749907205
 
1.6%
3.595981847196
 
1.5%
3.85130798196
 
1.5%
3.774316766195
 
1.5%
3.49080423191
 
1.5%
3.921954578179
 
1.4%
3.232332075174
 
1.4%
3.37111026172
 
1.3%
Other values (2146)9916
77.5%
ValueCountFrequency (%)
1.5709469251
< 0.1%
1.6324789811
< 0.1%
1.744140461
< 0.1%
1.8555206231
< 0.1%
1.8898312841
< 0.1%
1.9754927591
< 0.1%
2.0154872972
< 0.1%
2.0222533981
< 0.1%
2.0538006672
< 0.1%
2.1259038871
< 0.1%
ValueCountFrequency (%)
4.6655244791159
9.1%
4.6612956742
 
< 0.1%
4.6594690427
 
0.1%
4.658464511
 
< 0.1%
4.6527731921
 
< 0.1%
4.6518208531
 
< 0.1%
4.6504699491
 
< 0.1%
4.649912906103
 
0.8%
4.644162755
 
< 0.1%
4.6413727335
 
< 0.1%

area_type_Carpet Area
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12704 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

Length

2025-09-30T00:34:56.363846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.379739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

Most occurring characters

ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012704
99.3%
187
 
0.7%

area_type_Plot Area
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
10802 
1
1989 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

Length

2025-09-30T00:34:56.399898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.416079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

Most occurring characters

ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010802
84.5%
11989
 
15.5%

area_type_Super built-up Area
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
1
8317 
0
4474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18317
65.0%
04474
35.0%

Length

2025-09-30T00:34:56.436589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.453111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
18317
65.0%
04474
35.0%

Most occurring characters

ValueCountFrequency (%)
18317
65.0%
04474
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18317
65.0%
04474
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18317
65.0%
04474
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18317
65.0%
04474
35.0%

location_Hebbal
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12630 
1
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

Length

2025-09-30T00:34:56.475287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.493788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

Most occurring characters

ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012630
98.7%
1161
 
1.3%

location_Kanakpura Road
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12542 
1
 
249

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

Length

2025-09-30T00:34:56.515758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.531567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

Most occurring characters

ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012542
98.1%
1249
 
1.9%

location_Marathahalli
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12622 
1
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

Length

2025-09-30T00:34:56.551881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.567709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

Most occurring characters

ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012622
98.7%
1169
 
1.3%

location_Raja Rajeshwari Nagar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12632 
1
 
159

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

Length

2025-09-30T00:34:56.587439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.725976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

Most occurring characters

ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012632
98.8%
1159
 
1.2%

location_Sarjapur Road
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12412 
1
 
379

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

Length

2025-09-30T00:34:56.792741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.838477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

Most occurring characters

ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012412
97.0%
1379
 
3.0%

location_Thanisandra
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12562 
1
 
229

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

Length

2025-09-30T00:34:56.862423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.879718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

Most occurring characters

ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012562
98.2%
1229
 
1.8%

location_Uttarahalli
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12634 
1
 
157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

Length

2025-09-30T00:34:56.908514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.931087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

Most occurring characters

ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012634
98.8%
1157
 
1.2%

location_Whitefield
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12267 
1
 
524

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

Length

2025-09-30T00:34:56.952913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:56.971628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

Most occurring characters

ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012267
95.9%
1524
 
4.1%

location_Yelahanka
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
0
12581 
1
 
210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

Length

2025-09-30T00:34:56.992827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:57.011255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

Most occurring characters

ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012581
98.4%
1210
 
1.6%

location_others
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.9 KiB
1
10267 
0
2524 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12791
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Length

2025-09-30T00:34:57.034648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T00:34:57.056566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Most occurring characters

ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
110267
80.3%
02524
 
19.7%

Interactions

2025-09-30T00:34:55.164529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.381469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.696828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.923742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.202993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.417567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.649097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.872050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.194937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.422273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.724521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.949680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.230432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.445806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.676155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.897854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.225908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.469591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.753286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.978887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.258231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.475516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.705158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.924746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.256061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.515863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.780617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.004595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.285576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.503538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.732511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.021983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.285095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.548890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.808165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.092709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.310596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.532188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.760782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.049319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.315099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.591340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.837954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.122014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.338151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.561220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.789811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.080521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.343716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.641546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.867324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.149571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.365221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.591707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.817485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.109407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.372506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.668803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:53.894680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.175329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.390760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.619502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:54.843956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T00:34:55.136008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-30T00:34:57.087562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
area_type_Carpet Areaarea_type_Plot Areaarea_type_Super built-up Areabalconybalcony_per_bhkbathbath_per_bhkbhklocation_Hebballocation_Kanakpura Roadlocation_Marathahallilocation_Raja Rajeshwari Nagarlocation_Sarjapur Roadlocation_Thanisandralocation_Uttarahallilocation_Whitefieldlocation_Yelahankalocation_otherspriceprice_per_balconyprice_per_bhkprice_per_sqftsqft_per_bhktotal_sqft
area_type_Carpet Area1.0000.0330.1110.0200.0810.0270.0000.0290.0080.0250.0000.0000.0080.0000.0000.0120.0000.0100.0000.0000.0000.0000.0450.093
area_type_Plot Area0.0331.0000.5850.1170.3690.4450.2650.5020.0370.0400.0260.0280.0320.0500.0280.0000.0240.1080.1870.3660.2130.5100.5730.222
area_type_Super built-up Area0.1110.5851.0000.1160.3410.3380.2000.4010.0350.0560.0340.0390.0460.0590.0200.0130.0230.1210.1260.2720.1610.3390.4070.220
balcony0.0200.1170.1161.0000.5330.2430.1200.2410.0000.0270.0280.0130.0180.0320.0230.0150.0000.0240.0930.2720.1020.1050.1380.232
balcony_per_bhk0.0810.3690.3410.5331.0000.3010.1240.3990.0300.0940.0440.0430.0330.0360.0100.0420.0140.093-0.226-0.636-0.027-0.2100.198-0.089
bath0.0270.4450.3380.2430.3011.0000.4510.7150.0410.0640.0190.0410.0320.0350.0630.0220.0140.0450.1700.2870.2310.2680.2980.522
bath_per_bhk0.0000.2650.2000.1200.1240.4511.0000.4110.0760.0380.0240.0260.0400.0430.0820.0650.0000.0460.1310.1180.3050.1340.3850.072
bhk0.0290.5020.4010.2410.3990.7150.4111.0000.0470.0620.0210.0380.0430.0500.0400.0590.0270.0860.1730.2860.2000.2620.3640.536
location_Hebbal0.0080.0370.0350.0000.0300.0410.0760.0471.0000.0100.0050.0030.0150.0090.0030.0200.0080.2270.0510.0680.0720.0550.0640.061
location_Kanakpura Road0.0250.0400.0560.0270.0940.0640.0380.0620.0101.0000.0110.0100.0210.0140.0100.0260.0130.2830.0100.0600.0330.0960.0690.063
location_Marathahalli0.0000.0260.0340.0280.0440.0190.0240.0210.0050.0111.0000.0040.0160.0100.0040.0200.0080.2320.0000.0400.0390.0470.0330.008
location_Raja Rajeshwari Nagar0.0000.0280.0390.0130.0430.0410.0260.0380.0030.0100.0041.0000.0150.0090.0030.0190.0080.2250.0000.0720.0800.0880.0590.064
location_Sarjapur Road0.0080.0320.0460.0180.0330.0320.0400.0430.0150.0210.0160.0151.0000.0200.0150.0340.0190.3520.0000.0270.0390.0500.0610.049
location_Thanisandra0.0000.0500.0590.0320.0360.0350.0430.0500.0090.0140.0100.0090.0201.0000.0090.0250.0120.2710.0000.0590.0660.0700.0660.046
location_Uttarahalli0.0000.0280.0200.0230.0100.0630.0820.0400.0030.0100.0040.0030.0150.0091.0000.0190.0080.2240.0000.0660.0960.0910.0700.068
location_Whitefield0.0120.0000.0130.0150.0420.0220.0650.0590.0200.0260.0200.0190.0340.0250.0191.0000.0240.4160.0200.0260.0530.0460.0990.075
location_Yelahanka0.0000.0240.0230.0000.0140.0140.0000.0270.0080.0130.0080.0080.0190.0120.0080.0241.0000.2600.0000.0380.0500.0610.0510.015
location_others0.0100.1080.1210.0240.0930.0450.0460.0860.2270.2830.2320.2250.3520.2710.2240.4160.2601.0000.0230.0660.0680.1340.1250.062
price0.0000.1870.1260.093-0.2260.1700.1310.1730.0510.0100.0000.0000.0000.0000.0000.0200.0000.0231.0000.7940.7880.8070.2860.735
price_per_balcony0.0000.3660.2720.272-0.6360.2870.1180.2860.0680.0600.0400.0720.0270.0590.0660.0260.0380.0660.7941.0000.7000.7190.2410.498
price_per_bhk0.0000.2130.1610.102-0.0270.2310.3050.2000.0720.0330.0390.0800.0390.0660.0960.0530.0500.0680.7880.7001.0000.7700.5890.505
price_per_sqft0.0000.5100.3390.105-0.2100.2680.1340.2620.0550.0960.0470.0880.0500.0700.0910.0460.0610.1340.8070.7190.7701.0000.0720.273
sqft_per_bhk0.0450.5730.4070.1380.1980.2980.3850.3640.0640.0690.0330.0590.0610.0660.0700.0990.0510.1250.2860.2410.5890.0721.0000.482
total_sqft0.0930.2220.2200.232-0.0890.5220.0720.5360.0610.0630.0080.0640.0490.0460.0680.0750.0150.0620.7350.4980.5050.2730.4821.000

Missing values

2025-09-30T00:34:55.425570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-30T00:34:55.495463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

total_sqftbathbalconypricebhkprice_per_sqftsqft_per_bhkbath_per_bhkbalcony_per_bhkprice_per_bhkprice_per_balconyarea_type_Carpet Areaarea_type_Plot Areaarea_type_Super built-up Arealocation_Hebballocation_Kanakpura Roadlocation_Marathahallilocation_Raja Rajeshwari Nagarlocation_Sarjapur Roadlocation_Thanisandralocation_Uttarahallilocation_Whitefieldlocation_Yelahankalocation_others
016.2446870.7441361.00000039.070.833999-2.300598385.3204741.0000000.5000002.6777563.4697520010000000001
120.7043961.7581883.000000120.002.028481-2.195390468.7403081.2500000.7500003.0193883.4908040100000000001
217.6945910.7441363.00000062.001.480245-2.229040352.1961590.6666671.0000002.7232032.8941400000000001000
317.9606831.2303651.00000095.001.480245-2.042368370.8516881.0000000.3333333.0617074.2545820010000000001
416.8305010.7441361.00000051.000.833999-2.235269434.6758371.0000000.5000002.8911953.7073400010000000001
516.7131870.7441361.00000038.000.833999-2.360097424.4240661.0000000.5000002.6552793.4448830010000000100
620.7043961.6003721.582308204.002.028481-1.946844491.1366261.0000000.3955773.4277254.5194780010000000001
720.7043961.6003721.582308600.002.028481-1.669964586.8109141.0000000.3955773.6806364.6655240010000000001
817.2419111.2303651.00000063.251.480245-2.173263322.1224731.0000000.3333332.7392673.8978850010010000000
916.0885141.7581881.582308370.002.277616-1.669964184.5202181.0000000.2637183.5701514.6655240100000000001
total_sqftbathbalconypricebhkprice_per_sqftsqft_per_bhkbath_per_bhkbalcony_per_bhkprice_per_bhkprice_per_balconyarea_type_Carpet Areaarea_type_Plot Areaarea_type_Super built-up Arealocation_Hebballocation_Kanakpura Roadlocation_Marathahallilocation_Raja Rajeshwari Nagarlocation_Sarjapur Roadlocation_Thanisandralocation_Uttarahallilocation_Whitefieldlocation_Yelahankalocation_others
1330718.8137501.2303653.000000134.001.480245-1.950013435.8139731.001.0000003.3271333.5893540000000000001
1330817.9799481.2303651.000000142.001.480245-1.823885372.2311851.000.3333333.3712414.6027630000000000001
1330918.4373831.2303651.58230892.131.480245-2.107984406.1596901.000.5274363.0377163.8247610010000000001
1331016.2189510.7441362.00000052.710.833999-2.153961383.2556371.001.0000002.9173293.1152100010000000001
1331217.0658500.7441362.00000047.000.833999-2.297544455.8148871.001.0000002.8262043.0111610010000000001
1331418.5555551.2303653.000000112.001.480245-2.018894415.2982271.001.0000003.1895553.4290210010000000001
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Duplicate rows

Most frequently occurring

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